TY - JOUR
T1 - Assessing prostate cancer risk
T2 - Results from the Prostate Cancer Prevention Trial
AU - Thompson, Ian M.
AU - Ankerst, Donna Pauler
AU - Chi, Chen
AU - Goodman, Phyllis J.
AU - Tangen, Catherine M.
AU - Lucia, M. Scott
AU - Feng, Ziding
AU - Parnes, Howard L.
AU - Coltman, Charles A.
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2006/4/19
Y1 - 2006/4/19
N2 - Background: Prostate-specific antigen (PSA) testing is the primary method used to diagnose prostate cancer in the United States. Methods to integrate other risk factors associated with prostate cancer into individualized risk prediction are needed. We used prostate biopsy data from men who participated in the Prostate Cancer Prevention Trial (PCPT) to develop a predictive model of prostate cancer. Methods: We included 5519 men from the placebo group of the PCPT who underwent prostate biopsy, had at least one PSA measurement and a digital rectal examination (DRE) performed during the year before the biopsy, and had at least two PSA measurements performed during the 3 years before the prostate biopsy. Logistic regression was used to model the risk of prostate cancer and high-grade disease associated with age at biopsy, race, family history of prostate cancer, PSA level, PSA velocity, DRE result, and previous prostate biopsy. Risk equations were created from the estimated logistic regression models. All statistical tests were two-sided. Results: A total of 1211 (21.9%) men were diagnosed with prostate cancer by prostate biopsy. Variables that predicted prostate cancer included higher PSA level, positive family history of prostate cancer, and abnormal DRE result, whereas a previous negative prostate biopsy was associated with reduced risk. Neither age at biopsy nor PSA velocity contributed independent prognostic information. Higher PSA level, abnormal DRE result, older age at biopsy, and African American race were predictive for high-grade disease (Gleason score ≥7) whereas a previous negative prostate biopsy reduced this risk. Conclusions: This predictive model allows an individualized assessment of prostate cancer risk and risk of high-grade disease for men who undergo a prostate biopsy.
AB - Background: Prostate-specific antigen (PSA) testing is the primary method used to diagnose prostate cancer in the United States. Methods to integrate other risk factors associated with prostate cancer into individualized risk prediction are needed. We used prostate biopsy data from men who participated in the Prostate Cancer Prevention Trial (PCPT) to develop a predictive model of prostate cancer. Methods: We included 5519 men from the placebo group of the PCPT who underwent prostate biopsy, had at least one PSA measurement and a digital rectal examination (DRE) performed during the year before the biopsy, and had at least two PSA measurements performed during the 3 years before the prostate biopsy. Logistic regression was used to model the risk of prostate cancer and high-grade disease associated with age at biopsy, race, family history of prostate cancer, PSA level, PSA velocity, DRE result, and previous prostate biopsy. Risk equations were created from the estimated logistic regression models. All statistical tests were two-sided. Results: A total of 1211 (21.9%) men were diagnosed with prostate cancer by prostate biopsy. Variables that predicted prostate cancer included higher PSA level, positive family history of prostate cancer, and abnormal DRE result, whereas a previous negative prostate biopsy was associated with reduced risk. Neither age at biopsy nor PSA velocity contributed independent prognostic information. Higher PSA level, abnormal DRE result, older age at biopsy, and African American race were predictive for high-grade disease (Gleason score ≥7) whereas a previous negative prostate biopsy reduced this risk. Conclusions: This predictive model allows an individualized assessment of prostate cancer risk and risk of high-grade disease for men who undergo a prostate biopsy.
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U2 - 10.1093/jnci/djj131
DO - 10.1093/jnci/djj131
M3 - Article
C2 - 16622122
AN - SCOPUS:33646459214
VL - 98
SP - 529
EP - 534
JO - Journal of the National Cancer Institute
JF - Journal of the National Cancer Institute
SN - 0027-8874
IS - 8
ER -